There has been much recent progress in forecasting the next observation of a
linear dynamical system (LDS), which is known as the improper learning, as well
as in the estimation of its system matrices, which is known as the proper
learning of LDS. We present an approach to proper learning of LDS, which in
spite of the non-convexity of the problem, guarantees global convergence of
numerical solutions to a least-squares estimator. We present promising
computational results.
( 2
min )
Motivation: We explored how explainable AI (XAI) can help to shed light into
the inner workings of neural networks for protein function prediction, by
extending the widely used XAI method of integrated gradients such that latent
representations inside of transformer models, which were finetuned to Gene
Ontology term and Enzyme Commission number prediction, can be inspected too.
Results: The approach enabled us to identify amino acids in the sequences that
the transformers pay particular attention to, and to show that these relevant
sequence parts reflect expectations from biology and chemistry, both in the
embedding layer and inside of the model, where we identified transformer heads
with a statistically significant correspondence of attribution maps with ground
truth sequence annotations (e.g., transmembrane regions, active sites) across
many proteins. Availability and Implementation: Source code can be accessed at
https://github.com/markuswenzel/xai-proteins .
( 2
min )
We study the problem of estimating mixtures of Gaussians under the constraint
of differential privacy (DP). Our main result is that $\tilde{O}(k^2 d^4
\log(1/\delta) / \alpha^2 \varepsilon)$ samples are sufficient to estimate a
mixture of $k$ Gaussians up to total variation distance $\alpha$ while
satisfying $(\varepsilon, \delta)$-DP. This is the first finite sample
complexity upper bound for the problem that does not make any structural
assumptions on the GMMs.
To solve the problem, we devise a new framework which may be useful for other
tasks. On a high level, we show that if a class of distributions (such as
Gaussians) is (1) list decodable and (2) admits a "locally small'' cover
[BKSW19] with respect to total variation distance, then the class of its
mixtures is privately learnable. The proof circumvents a known barrier
indicating that, unlike Gaussians, GMMs do not admit a locally small cover
[AAL21].
( 2
min )
This paper presents a novel reconstruction method that leverages Diffusion
Models to protect machine learning classifiers against adversarial attacks, all
without requiring any modifications to the classifiers themselves. The
susceptibility of machine learning models to minor input perturbations renders
them vulnerable to adversarial attacks. While diffusion-based methods are
typically disregarded for adversarial defense due to their slow reverse
process, this paper demonstrates that our proposed method offers robustness
against adversarial threats while preserving clean accuracy, speed, and
plug-and-play compatibility. Code at:
https://github.com/HondamunigePrasannaSilva/DiffDefence.
( 2
min )
Multiscale stochastic dynamical systems have been widely adopted to
scientific and engineering problems due to their capability of depicting
complex phenomena in many real world applications. This work is devoted to
investigating the effective reduced dynamics for a slow-fast stochastic
dynamical system. Given observation data on a short-term period satisfying some
unknown slow-fast stochastic system, we propose a novel algorithm including a
neural network called Auto-SDE to learn invariant slow manifold. Our approach
captures the evolutionary nature of a series of time-dependent autoencoder
neural networks with the loss constructed from a discretized stochastic
differential equation. Our algorithm is also proved to be accurate, stable and
effective through numerical experiments under various evaluation metrics.
( 2
min )
In this work, we proposed a novel and general method to construct tight
frames on graphs with compact supports based on a series of hierarchical
partitions. Starting from our abstract construction that generalizes previous
methods based on partition trees, we are able to flexibly incorporate subgraph
Laplacians into our design of graph frames. Consequently, our general methods
permit adjusting the (subgraph) vanishing moments of the framelets and extra
properties, such as directionality, for efficiently representing graph signals
with path-like supports. Several variants are explicitly defined and tested.
Experimental results show our proposed graph frames perform superiorly in
non-linear approximation tasks.
( 2
min )
Multiagent systems aim to accomplish highly complex learning tasks through
decentralised consensus seeking dynamics and their use has garnered a great
deal of attention in the signal processing and computational intelligence
societies. This article examines the behaviour of multiagent networked systems
with nonlinear filtering/learning dynamics. To this end, a general formulation
for the actions of an agent in multiagent networked systems is presented and
conditions for achieving a cohesive learning behaviour is given. Importantly,
application of the so derived framework in distributed and federated learning
scenarios are presented.
( 2
min )
A cross-departmental team is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings.
( 10
min )
The PhD student is honing algorithms for designing large structures with less material — helping to shrink the construction industry’s huge carbon footprint.
( 10
min )
The world’s largest democracy is poised to transform itself and the world, embracing AI on an enormous scale. Speaking with the press Friday in Bengaluru, in the context of announcements from two of India’s largest conglomerates, Reliance Industries Limited and Tata Group, NVIDIA founder and CEO Jensen Huang detailed plans to bring AI technology and Read article >
( 6
min )
In this post, we’ll take you on a journey to rapidly build and deploy a document search indexing solution that helps your organization to better harness and extract insights from documents. Whether you're in Human Resources looking for specific clauses in employee contracts, or a financial analyst sifting through a mountain of invoices to extract payment data, this solution is tailored to empower you to access the information you need with unprecedented speed and accuracy.
( 11
min )
Digital publishers are continuously looking for ways to streamline and automate their media workflows in order to generate and publish new content as rapidly as they can. Publishers can have repositories containing millions of images and in order to save money, they need to be able to reuse these images across articles. Finding the image that best matches an article in repositories of this scale can be a time-consuming, repetitive, manual task that can be automated. It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS). In this post, we demonstrate how to use Amazon Rekognition, Amazon SageMaker JumpStart, and Amazon OpenSearch Service to solve this business problem.
( 10
min )
Machine learning (ML) is transforming every industry, process, and business, but the path to success is not always straightforward. In this blog post, we demonstrate how Duke Energy, a Fortune 150 company headquartered in Charlotte, NC., collaborated with the AWS Machine Learning Solutions Lab (MLSL) to use computer vision to automate the inspection of wooden utility poles and help prevent power outages, property damage and even injuries.
( 13
min )
Gender, race, and age disparities in AI-generated images persist. This AIES 2023 study on text-to-image models shows that even basic prompts can lead to underrepresentation, calling for responsible bias mitigation strategies.
The post Understanding social biases through the text-to-image generation lens appeared first on Microsoft Research.
( 10
min )
Every year, interns help advance research at Microsoft. In “Intern Insights,” PhD students Anunay Kulshrestha and Karan Newatia talk with cryptographer Josh Benaloh about working on the verifiable election technology ElectionGuard.
The post Intern Insights: Dr. Josh Benaloh with Anunay Kulshrestha and Karan Newatia appeared first on Microsoft Research.
( 30
min )
Thanks to rapid technological advances, consumers have become accustomed to an unprecedented level of convenience and efficiency. Smartphones make it easier than ever to search for a product and have it delivered right to the front door. Video chat technology lets friends and family on different continents connect with ease. With voice command tools, AI Read article >
( 12
min )
GeForce NOW brings expanded support for PC Game Pass to members this week. Members can stream eight more games from Microsoft’s subscription service, including four titles from hit publisher Focus Entertainment. Play A Plague Tale: Requiem, Atomic Heart and more from the GeForce NOW library at up to 4K resolution and 120 frames per second Read article >
( 5
min )
In this post, we will build an end-to-end solution to find optimal control policies using only historical data on Amazon SageMaker using Ray’s RLlib library. To learn more about reinforcement learning, see Use Reinforcement Learning with Amazon SageMaker.
( 10
min )
This post details how to set up container-based GPU metrics and provides an example of collecting these metrics from EKS pods.
( 15
min )
In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. We also discuss some common design scenarios and patterns when building SageMaker Pipelines and provide examples for addressing them.
( 11
min )
Retrosynthesis analysis is a critical task in organic chemistry and central to many important industries. It primarily involves decomposing a target molecule into commercially available molecules step by step. Since synthesis strategies can be quite diverse and strategic, retrosynthesis planning with expert knowledge has long been considered an “art.” Recently, machine learning-based approaches have achieved […]
The post Incorporating chemists’ insight with AI models for single-step retrosynthesis prediction appeared first on Microsoft Research.
( 11
min )
New evaluation methods and a commitment to continual improvement are musts if we’re to build multimodal AI systems that advance human goals. Learn about cutting-edge research into the responsible development and use of multimodal AI at Microsoft.
The post Frontiers of multimodal learning: A responsible AI approach appeared first on Microsoft Research.
( 25
min )
In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG). You can use this post as a reference to build secure enterprise applications in the Generative AI domain using AWS services.
( 7
min )
This post shows you how to configure the Amazon Kendra AEM connector to index your content and search your AEM assets and pages. The connector also ingests the access control list (ACL) information for each document. The ACL information is used to show search results filtered by what a user has access to.
( 11
min )
Today, we are excited to announce the capability to fine-tune Llama 2 models by Meta using Amazon SageMaker JumpStart. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned LLMs, called Llama-2-chat, are optimized for dialogue use cases.
( 46
min )
Recently, generative AI applications have captured widespread attention and imagination. Customers want to deploy generative AI models on GPUs but at the same time are conscious of costs. SageMaker MMEs support GPU instances and is a great option for these types of applications. Today, we are excited to announce TorchServe support for SageMaker MMEs. This new model server support gives you the advantage of all the benefits of MMEs while still using the serving stack that TorchServe customers are most familiar with. In this post, we demonstrate how to host generative AI models, such as Stable Diffusion and Segment Anything Model, on SageMaker MMEs using TorchServe and build a language-guided editing solution that can help artists and content creators develop and iterate their artwork faster.
( 12
min )
Before she entered high school, Ge Dong wanted to be a physicist like her mom, a professor at Shanghai Jiao Tong University.
( 6
min )
Rafi Nizam is an award-winning independent animator, director, character designer and more. He’s developed feature films at Sony Pictures, children’s series and comedies at BBC and global transmedia content at NBCUniversal.
( 8
min )
In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. We then discuss how Amazon SageMaker helps us with feature engineering and building a scalable supervised deep learning model.
( 10
min )
In this post, we target these situations and solve the problem of risking high costs by deploying large foundation models to Amazon SageMaker asynchronous endpoints from Amazon SageMaker JumpStart. This can help cut costs of the architecture, allowing the endpoint to run only when requests are in the queue and for a short time-to-live, while scaling down to zero when no requests are waiting to be serviced. This sounds great for a lot of use cases; however, an endpoint that has scaled down to zero will introduce a cold start time before being able to serve inferences.
( 10
min )
Microsoft researchers are proposing a new way to ensure greater trust and accountability in email, texts, direct messages on social platforms, even phone calls, to help mitigate sophisticated threats from AI-related scams and fraud.
The post Rethinking trust in direct messages in the AI era appeared first on Microsoft Research.
( 14
min )
With coral reefs in rapid decline across the globe, researchers from the University of Hawaii at Mānoa have pioneered an AI-based surveying tool that monitors reef health from the sky. Using deep learning models and high-resolution satellite imagery powered by NVIDIA GPUs, the researchers have developed a new method for spotting and tracking coral reef Read article >
( 6
min )
Creating 3D scans of physical products can be time consuming. Businesses often use traditional methods, like photogrammetry-based apps and scanners, but these can take hours or even days. They also don’t always provide the 3D quality and level of detail needed to make models look realistic in all its applications. Italy-based startup Covision Media is Read article >
( 7
min )
Underscoring NVIDIA’s growing relationship with the global technology superpower, Indian Prime Minister Narendra Modi met with NVIDIA founder and CEO Jensen Huang Monday evening. The meeting at 7 Lok Kalyan Marg — as the Prime Minister’s official residence in New Delhi is known — comes as Modi prepares to host a gathering of leaders from Read article >
( 5
min )
Knowledge graphs are powerful tools for representing and organising complex
biomedical data. Several knowledge graph embedding algorithms have been
proposed to learn from and complete knowledge graphs. However, a recent study
demonstrates the limited efficacy of these embedding algorithms when applied to
biomedical knowledge graphs, raising the question of whether knowledge graph
embeddings have limitations in biomedical settings. This study aims to apply
state-of-the-art knowledge graph embedding models in the context of a recent
biomedical knowledge graph, BioKG, and evaluate their performance and potential
downstream uses. We achieve a three-fold improvement in terms of performance
based on the HITS@10 score over previous work on the same biomedical knowledge
graph. Additionally, we provide interpretable predictions through a rule-based
method. We demonstrate that knowledge graph embedding models are applicable in
practice by evaluating the best-performing model on four tasks that represent
real-life polypharmacy situations. Results suggest that knowledge learnt from
large biomedical knowledge graphs can be transferred to such downstream use
cases. Our code is available at https://github.com/aryopg/biokge.
( 3
min )
In reinforcement learning (RL), key components of many algorithms are the
exploration strategy and replay buffer. These strategies regulate what
environment data is collected and trained on and have been extensively studied
in the RL literature. In this paper, we investigate the impact of these
components in the context of generalisation in multi-task RL. We investigate
the hypothesis that collecting and training on more diverse data from the
training environments will improve zero-shot generalisation to new tasks. We
motivate mathematically and show empirically that generalisation to tasks that
are "reachable'' during training is improved by increasing the diversity of
transitions in the replay buffer. Furthermore, we show empirically that this
same strategy also shows improvement for generalisation to similar but
"unreachable'' tasks which could be due to improved generalisation of the
learned latent representations.
( 2
min )
We present the Multi-Modal Discussion Transformer (mDT), a novel multi-modal
graph-based transformer model for detecting hate speech in online social
networks, such as Reddit discussions. In contrast to traditional comment-only
methods, our approach to labelling a comment as hate speech involves a holistic
analysis of text and images grounded in the discussion context. This is done by
leveraging graph transformers to capture the contextual relationships in the
entire discussion surrounding a comment and grounding the interwoven fusion
layers that combine individual comments' text and image embeddings instead of
processing modalities separately. We compare the performance of our model to
baselines that only process individual comments and conduct extensive ablation
studies. To evaluate our work, we present a new dataset, HatefulDiscussions,
comprising complete multi-modal discussions from multiple online communities on
Reddit. We conclude with future work for multimodal solutions to deliver social
value in online contexts, arguing that capturing a holistic view of a
conversation significantly advances the effort to detect anti-social behaviour.
( 2
min )
The advent of novel 5G services and applications with binding latency
requirements and guaranteed Quality of Service (QoS) hastened the need to
incorporate autonomous and proactive decision-making in network management
procedures. The objective of our study is to provide a thorough analysis of
predictive latency within 5G networks by utilizing real-world network data that
is accessible to mobile network operators (MNOs). In particular, (i) we present
an analytical formulation of the user-plane latency as a Hypoexponential
distribution, which is validated by means of a comparative analysis with
empirical measurements, and (ii) we conduct experimental results of
probabilistic regression, anomaly detection, and predictive forecasting
leveraging on emerging domains in Machine Learning (ML), such as Bayesian
Learning (BL) and Machine Learning on Graphs (GML). We test our predictive
framework using data gathered from scenarios of vehicular mobility, dense-urban
traffic, and social gathering events. Our results provide valuable insights
into the efficacy of predictive algorithms in practical applications.
( 2
min )
Pre-trained large language models demonstrate potential in extracting
information from DNA sequences, yet adapting to a variety of tasks and data
modalities remains a challenge. To address this, we propose DNAGPT, a
generalized DNA pre-training model trained on over 200 billion base pairs from
all mammals. By enhancing the classic GPT model with a binary classification
task (DNA sequence order), a numerical regression task (guanine-cytosine
content prediction), and a comprehensive token language, DNAGPT can handle
versatile DNA analysis tasks while processing both sequence and numerical data.
Our evaluation of genomic signal and region recognition, mRNA abundance
regression, and artificial genomes generation tasks demonstrates DNAGPT's
superior performance compared to existing models designed for specific
downstream tasks, benefiting from pre-training using the newly designed model
structure.
( 2
min )
We study the use of binary activated neural networks as interpretable and
explainable predictors in the context of regression tasks on tabular data; more
specifically, we provide guarantees on their expressiveness, present an
approach based on the efficient computation of SHAP values for quantifying the
relative importance of the features, hidden neurons and even weights. As the
model's simplicity is instrumental in achieving interpretability, we propose a
greedy algorithm for building compact binary activated networks. This approach
doesn't need to fix an architecture for the network in advance: it is built one
layer at a time, one neuron at a time, leading to predictors that aren't
needlessly complex for a given task.
( 2
min )
We propose to apply several gradient estimation techniques to enable the
differentiation of programs with discrete randomness in High Energy Physics.
Such programs are common in High Energy Physics due to the presence of
branching processes and clustering-based analysis. Thus differentiating such
programs can open the way for gradient based optimization in the context of
detector design optimization, simulator tuning, or data analysis and
reconstruction optimization. We discuss several possible gradient estimation
strategies, including the recent Stochastic AD method, and compare them in
simplified detector design experiments. In doing so we develop, to the best of
our knowledge, the first fully differentiable branching program.
( 2
min )
These lecture notes provide an overview of existing methodologies and recent
developments for estimation and inference with high dimensional time series
regression models. First, we present main limit theory results for high
dimensional dependent data which is relevant to covariance matrix structures as
well as to dependent time series sequences. Second, we present main aspects of
the asymptotic theory related to time series regression models with many
covariates. Third, we discuss various applications of statistical learning
methodologies for time series analysis purposes.
( 2
min )
Implicit neural networks have demonstrated remarkable success in various
tasks. However, there is a lack of theoretical analysis of the connections and
differences between implicit and explicit networks. In this paper, we study
high-dimensional implicit neural networks and provide the high dimensional
equivalents for the corresponding conjugate kernels and neural tangent kernels.
Built upon this, we establish the equivalence between implicit and explicit
networks in high dimensions.
( 2
min )
We’re excited to announce the availability of response streaming through Amazon SageMaker real-time inference. Now you can continuously stream inference responses back to the client when using SageMaker real-time inference to help you build interactive experiences for generative AI applications such as chatbots, virtual assistants, and music generators. With this new feature, you can start streaming the responses immediately when they’re available instead of waiting for the entire response to be generated. This lowers the time-to-first-byte for your generative AI applications. In this post, we’ll show how to build a streaming web application using SageMaker real-time endpoints with the new response streaming feature for an interactive chat use case. We use Streamlit for the sample demo application UI.
( 12
min )
Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps). Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. The following figure illustrates the topics we discuss.
( 23
min )
MIT Plasma Science and Fusion Center will receive DoE support to improve access to fusion data and increase workforce diversity.
( 8
min )
Entrepreneurs are cultivating generative AI from the west coast of Africa to the eastern edge of the Arabian Desert. Gen AI is the latest of the big plans Kofi Genfi and Nii Osae have been hatching since they met 15 years ago in high school in Accra, Ghana’s capital that sits on the Gulf of Read article >
( 7
min )
Academics Mory Gharib and Alireza Ramezani in 2020 were spitballing a transforming robot that is now getting a shot at work that’s literally out of this world: NASA Mars Rover missions. Caltech has unveiled its multi-talented robot that can fly, drive, walk and do eight permutations of motions through a combination of its skills. They Read article >
( 6
min )
Just like that, summer falls into September, and some of the most anticipated games of the year, like the Cyberpunk 2077: Phantom Liberty expansion, PAYDAY 3 and Party Animals, are dropping into the GeForce NOW library at launch this month. They’re part of 24 new games hitting the cloud gaming service in September. And the Read article >
( 8
min )
In this episode of the Microsoft Research Podcast, Managing Director of Microsoft Research India Sriram Rajamani discusses how generative AI is impacting the lab’s approach to research and how the country’s many languages can help advance conversational systems.
The post AI Frontiers: AI in India and beyond with Sriram Rajamani appeared first on Microsoft Research.
( 30
min )
Powered by Amazon Lex, the QnABot on AWS solution is an open-source, multi-channel, multi-language conversational chatbot. QnABot allows you to quickly deploy self-service conversational AI into your contact center, websites, and social media channels, reducing costs, shortening hold times, and improving customer experience and brand sentiment. In this post, we introduce the new Generative AI features for QnABot and walk through a tutorial to create, deploy, and customize QnABot to use these features. We also discuss some relevant use cases.
( 13
min )
This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. LLMs have demonstrated remarkable capabilities in natural language understanding and generation, serving as foundation models that can be adapted to various domains and tasks. There are significant benefits to using a pre-trained model. It reduces computation costs, reduces carbon footprints, and allows you to use state-of-the-art models without having to train one from scratch.
( 13
min )
In this issue: An illusion of predictability in scientific results; Kathleen Sullivan named to Insider’s 30 under 40 in healthcare list; FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations.
The post Research Focus: Week of August 28, 2023 appeared first on Microsoft Research.
( 9
min )
Each year, nearly 32 million people travel through the Bengaluru Airport, or BLR, one of the busiest airports in the world’s most populous nation. To provide such multitudes with a safer, quicker experience, the airport in the city formerly known as Bangalore is tapping vision AI technologies powered by Industry.AI. A member of the NVIDIA Read article >
( 6
min )
In the global entertainment landscape, TV show and film production stretches far beyond Hollywood or Bollywood — it’s a worldwide phenomenon. However, while streaming platforms have broadened the reach of content, dubbing and translation technology still has plenty of room for growth. Deepdub acts as a digital bridge, providing access to content by using generative Read article >
( 5
min )
In the dynamic landscape of modern business, the art of seamless data migration has evolved into a strategic imperative. As you navigate the intricacies of workspace transformations, you’re met with a complex interplay of technological advancements and operational demands Enter the era of leveraging Artificial Intelligence (AI) to redefine data migration – an approach that… Read More »Data migration redefined: Leveraging AI trends for smooth workspace transitions
The post Data migration redefined: Leveraging AI trends for smooth workspace transitions appeared first on Data Science Central.
( 21
min )
Currently, the use of technology in shipping and logistics is leading the industry through a transformative era, driven by rapid technological advancements, undoubtedly marking a pivotal moment in the digital shipping evolution. From automating routine processes to employing intelligent algorithms that predict and optimize routes, the technological revolution is redefining the way goods are transported… Read More »The future of shipping: How technology is shaping logistics and fulfillment
The post The future of shipping: How technology is shaping logistics and fulfillment appeared first on Data Science Central.
( 23
min )
In the early days of the Internet, there were four ‘horsemen’ of the Internet With IBM’s 4.5 billion investment in Hugging face today, the generative AI landscape is becoming a bit clearer. There are four Generative AI leaders emerging – others lagging – and one unknown Lets look at the four leaders of Generative AI… Read More »Generative AI megatrends: The four horsemen of Generative AI
The post Generative AI megatrends: The four horsemen of Generative AI appeared first on Data Science Central.
( 18
min )
There seems to be an app for everything, and mental health is no exception. According to a report, the global mental health apps market size was valued at $5.2 billion in 2022 and is predicted to reach $26.36 billion by 2032, at a CAGR of 17.7% during the forecast period. Mental health apps have emerged… Read More »The power of digital solutions: How mental health apps are transforming patient care
The post The power of digital solutions: How mental health apps are transforming patient care appeared first on Data Science Central.
( 20
min )
Introduction In our rapidly digitizing world, how businesses and systems communicate is paramount. The bedrock of this communication lies in data exchange methods, which allow seamless information flow, driving operational efficiencies and enabling innovation. Over the years, various data exchange protocols have emerged, each boasting unique strengths and presenting challenges. As enterprises strive to integrate… Read More »Modern data exchange methods: Exploring the strengths and limitations of leading protocols
The post Modern data exchange methods: Exploring the strengths and limitations of leading protocols appeared first on Data Science Central.
( 23
min )
Dramatic gains in hardware performance have spawned generative AI, and a rich pipeline of ideas for future speedups will drive machine learning to new heights, Bill Dally, NVIDIA’s chief scientist and senior vice president of research, said today in a keynote. Dally described a basket of techniques in the works — some already showing impressive Read article >
( 6
min )
As generative AI and large language models (LLMs) continue to drive innovations, compute requirements for training and inference have grown at an astonishing pace. To meet that need, Google Cloud today announced the general availability of its new A3 instances, powered by NVIDIA H100 Tensor Core GPUs. These GPUs bring unprecedented performance to all kinds Read article >
( 6
min )
Janice K. Lee, a.k.a Janice.Journal — the subject of this week’s In the NVIDIA Studio installment — is a TikTok sensation using AI to accelerate her creative process, find inspiration and automate repetitive tasks.
( 8
min )
In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker, Amazon EventBridge, AWS Lambda, Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD. The presented MLOps workflow provides a reusable template for managing the ML lifecycle through automation, monitoring, auditability, and scalability, thereby reducing the complexities and costs of maintaining batch inference workloads in production.
( 15
min )
As part of the 2023 Data Science Conference (DSCO 23), AWS partnered with the Data Institute at the University of San Francisco (USF) to conduct a datathon. Participants, both high school and undergraduate students, competed on a data science project that focused on air quality and sustainability. The Data Institute at the USF aims to support cross-disciplinary research and education in the field of data science. The Data Institute and the Data Science Conference provide a distinctive fusion of cutting-edge academic research and the entrepreneurial culture of the technology industry in the San Francisco Bay Area.
( 5
min )
Posted by Dahun Kim and Weicheng Kuo, Research Scientists, Google
The ability to detect objects in the visual world is crucial for computer vision and machine intelligence, enabling applications like adaptive autonomous agents and versatile shopping systems. However, modern object detectors are limited by the manual annotations of their training data, resulting in a vocabulary size significantly smaller than the vast array of objects encountered in reality. To overcome this, the open-vocabulary detection task (OVD) has emerged, utilizing image-text pairs for training and incorporating new category names at test time by associating them with the image content. By treating categories as text embeddings, open-vocabulary detectors can predict a wide range of unseen objects. Various techniqu…
( 93
min )
Posted by Dahun Kim and Weicheng Kuo, Research Scientists, Google
The ability to detect objects in the visual world is crucial for computer vision and machine intelligence, enabling applications like adaptive autonomous agents and versatile shopping systems. However, modern object detectors are limited by the manual annotations of their training data, resulting in a vocabulary size significantly smaller than the vast array of objects encountered in reality. To overcome this, the open-vocabulary detection task (OVD) has emerged, utilizing image-text pairs for training and incorporating new category names at test time by associating them with the image content. By treating categories as text embeddings, open-vocabulary detectors can predict a wide range of unseen objects. Various techniqu…
( 93
min )
Companies are discovering how accelerated computing can boost their bottom lines while making a positive impact on the planet. The NVIDIA RAPIDS Accelerator for Apache Spark, software that speeds data analytics, not only raises performance and lowers costs, it increases energy efficiency, too. That means it can help companies meet goals for net-zero emissions of Read article >
( 6
min )
AI Weirdness: the strange side of machine learning
( 2
min )
In the ever-evolving battle against the digital dark forces, the defenders of the virtual realm find themselves facing a barrage of ever-advancing threats. From the labyrinthine corridors of the Deep Web to the stealthy maneuvers of nation-state actors, the cyber landscape is as treacherous as it is vast. As our dependency on digital infrastructure deepens,… Read More »Empowering cyber guardians: How AI is changing the landscape of protection
The post Empowering cyber guardians: How AI is changing the landscape of protection appeared first on Data Science Central.
( 21
min )
We present an exact Bayesian inference method for discrete statistical
models, which can find exact solutions to many discrete inference problems,
even with infinite support and continuous priors. To express such models, we
introduce a probabilistic programming language that supports discrete and
continuous sampling, discrete observations, affine functions, (stochastic)
branching, and conditioning on events. Our key tool is probability generating
functions: they provide a compact closed-form representation of distributions
that are definable by programs, thus enabling the exact computation of
posterior probabilities, expectation, variance, and higher moments. Our
inference method is provably correct, fully automated and uses automatic
differentiation (specifically, Taylor polynomials), but does not require
computer algebra. Our experiments show that its performance on a range of
real-world examples is competitive with approximate Monte Carlo methods, while
avoiding approximation errors.
( 2
min )
Mode connectivity is a phenomenon where trained models are connected by a
path of low loss. We reframe this in the context of Information Geometry, where
neural networks are studied as spaces of parameterized distributions with
curved geometry. We hypothesize that shortest paths in these spaces, known as
geodesics, correspond to mode-connecting paths in the loss landscape. We
propose an algorithm to approximate geodesics and demonstrate that they achieve
mode connectivity.
( 2
min )
I study a stochastic multi-arm bandit problem where rewards are subject to
adversarial corruption. I propose a novel attack strategy that manipulates a
learner employing the UCB algorithm into pulling some non-optimal target arm $T
- o(T)$ times with a cumulative cost that scales as $\widehat{O}(\sqrt{\log
T})$, where $T$ is the number of rounds. I also prove the first lower bound on
the cumulative attack cost. The lower bound matches the upper bound up to
$O(\log \log T)$ factors, showing the proposed attack strategy to be near
optimal.
( 2
min )
We propose a novel master-slave architecture to solve the top-$K$
combinatorial multi-armed bandits problem with non-linear bandit feedback and
diversity constraints, which, to the best of our knowledge, is the first
combinatorial bandits setting considering diversity constraints under bandit
feedback. Specifically, to efficiently explore the combinatorial and
constrained action space, we introduce six slave models with distinguished
merits to generate diversified samples well balancing rewards and constraints
as well as efficiency. Moreover, we propose teacher learning based optimization
and the policy co-training technique to boost the performance of the multiple
slave models. The master model then collects the elite samples provided by the
slave models and selects the best sample estimated by a neural contextual
UCB-based network to make a decision with a trade-off between exploration and
exploitation. Thanks to the elaborate design of slave models, the co-training
mechanism among slave models, and the novel interactions between the master and
slave models, our approach significantly surpasses existing state-of-the-art
algorithms in both synthetic and real datasets for recommendation tasks. The
code is available at:
\url{https://github.com/huanghanchi/Master-slave-Algorithm-for-Top-K-Bandits}.
( 2
min )
This paper presents a set of industrial-grade text processing models for
Hungarian that achieve near state-of-the-art performance while balancing
resource efficiency and accuracy. Models have been implemented in the spaCy
framework, extending the HuSpaCy toolkit with several improvements to its
architecture. Compared to existing NLP tools for Hungarian, all of our
pipelines feature all basic text processing steps including tokenization,
sentence-boundary detection, part-of-speech tagging, morphological feature
tagging, lemmatization, dependency parsing and named entity recognition with
high accuracy and throughput. We thoroughly evaluated the proposed
enhancements, compared the pipelines with state-of-the-art tools and
demonstrated the competitive performance of the new models in all text
preprocessing steps. All experiments are reproducible and the pipelines are
freely available under a permissive license.
( 2
min )
A prevalent practice in recommender systems consists of averaging item
embeddings to represent users or higher-level concepts in the same embedding
space. This paper investigates the relevance of such a practice. For this
purpose, we propose an expected precision score, designed to measure the
consistency of an average embedding relative to the items used for its
construction. We subsequently analyze the mathematical expression of this score
in a theoretical setting with specific assumptions, as well as its empirical
behavior on real-world data from music streaming services. Our results
emphasize that real-world averages are less consistent for recommendation,
which paves the way for future research to better align real-world embeddings
with assumptions from our theoretical setting.
( 2
min )
In an age where data has become the lifeblood of businesses, deciphering this raw data to yield actionable insights is critical. Here is where the role of business analytics comes into play. Business analytics, a blend of data management, business intelligence, and predictive modeling, is a field dedicated to driving business strategies through the lens… Read More »Data visualization: The underrated skill in business analytics
The post Data visualization: The underrated skill in business analytics appeared first on Data Science Central.
( 22
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Today, we’re pleased to announce the preview of Amazon SageMaker Profiler, a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. With SageMaker Profiler, you can track all activities on CPUs and GPUs, such as CPU and GPU utilizations, kernel runs on GPUs, kernel launches on CPUs, sync operations, memory operations across GPUs, latencies between kernel launches and corresponding runs, and data transfer between CPUs and GPUs. In this post, we walk you through the capabilities of SageMaker Profiler.
( 9
min )
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
( 10
min )
The MIT and Accenture Convergence Initiative for Industry and Technology selects three new research projects to support.
( 9
min )
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
( 10
min )
As part of NVIDIA and Microsoft’s collaboration to bring more choice to gamers, new Microsoft Store integration has been added to GeForce NOW that lets gamers stream select titles from the Xbox PC Game Pass catalog on GeForce NOW, starting today. With the Microsoft Store integration, members will see a brand-new Xbox button on supported Read article >
( 8
min )
Mens, Manus and Machina (M3S) will design technology, training programs, and institutions for successful human-machine collaboration.
( 9
min )
Persistent Systems, a global digital engineering provider, has run several pilots and formal studies with Amazon CodeWhisperer that point to shifts in software engineering, generative AI-led modernization, responsible innovation, and more. This post highlights four themes emerging from Persistent’s Amazon CodeWhisperer experiments that could change software engineering as we know it.
( 8
min )
In this post, we walk you through importing data from, and exporting data to, an S3 access point in SageMaker Data Wrangler.
( 6
min )
In this post, we discuss how to implement federated learning on Amazon SageMaker to run ML with decentralized training data.
( 13
min )
MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
( 9
min )
On the eve of Gamescom, NVIDIA announced NVIDIA DLSS 3.5 featuring Ray Reconstruction — a new neural rendering AI model that creates more beautiful and realistic ray-traced visuals than traditional rendering methods — for real-time 3D creative apps and games.
( 8
min )
The latest advancements in AI for gaming are in the spotlight today at Gamescom, the world’s largest gaming conference, as NVIDIA introduced a host of technologies, starting with DLSS 3.5, the next step forward of its breakthrough AI neural rendering technology. DLSS 3.5, NVIDIA’s latest innovation in AI-powered graphics is an image quality upgrade incorporated Read article >
( 6
min )
Data science was a vaguely defined discipline to begin with, but it’s shaped up substantially lately. Execs now yearn to take immediate advantage of generative and other clearly useful (if currently problematic) kinds of AI. That demand suggests an opportunity for influencers and visionaries in organizations to lobby for each organization to build an AI-ready… Read More »Beyond data science: A knowledge foundation for the AI-ready enterprise
The post Beyond data science: A knowledge foundation for the AI-ready enterprise appeared first on Data Science Central.
( 21
min )
We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.
( 12
min )
Bill Dally — one of the world’s foremost computer scientists and head of NVIDIA’s research efforts — will describe the forces driving accelerated computing and AI in his keynote address at Hot Chips, an annual gathering of leading processor and system architects. Dally will detail advances in GPU silicon, systems and software that are delivering Read article >
( 5
min )
My journey continues as I integrate a GenAI tool (Bing AI) with my Thinking Like a Data Scientist (TLADS) methodology. In part 1 of this series, I used Bing AI to validate, augment, and enhance the first three steps in the TLADS methodology (Figure 1): And the results yielded a much deeper understanding of the… Read More »Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part II
The post Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part II appeared first on Data Science Central.
( 23
min )
The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.
( 8
min )
Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker, a fully managed ML service, with requirements to develop features offline in a code […]
( 12
min )
Microsoft Health Futures’ Javier Alvarez & oncologist Raj Jena have been collaborating for years on AI-assisted medical imaging. Today, their work is seeing real-world impact, helping doctors accelerate cancer patients’ access to treatment.
The post Collaborators: Project InnerEye with Javier Alvarez and Raj Jena appeared first on Microsoft Research.
( 31
min )
The verdict is in: A GeForce NOW Ultimate membership raises the bar on gaming. Members have been tackling the Ultimate KovvaK’s challenge head-on and seeing for themselves how the power of Ultimate improves their gaming with 240 frames per second streaming. The popular training title that helps gamers improve their aim fully launches in the Read article >
( 7
min )
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
( 8
min )
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
( 10
min )
MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. It’s natural to focus on a single model that you want to train and deploy. However, in reality, you’ll likely work with dozens or even hundreds of models, and the process may involve multiple complex steps. Therefore, it’s important […]
( 9
min )
In this post, we demonstrate how to train self-supervised vision transformers on overhead imagery using Amazon SageMaker. Travelers collaborated with the Amazon Machine Learning Solutions Lab (now known as the Generative AI Innovation Center) to develop this framework to support and enhance aerial imagery model use cases.
( 12
min )
In this post, we discuss how Thomson Reuters Labs created Open Arena, Thomson Reuters’s enterprise-wide large language model (LLM) playground that was developed in collaboration with AWS. The original concept came out of an AI/ML Hackathon supported by Simone Zucchet (AWS Solutions Architect) and Tim Precious (AWS Account Manager) and was developed into production using AWS services in under 6 weeks with support from AWS. AWS-managed services such as AWS Lambda, Amazon DynamoDB, and Amazon SageMaker, as well as the pre-built Hugging Face Deep Learning Containers (DLCs), contributed to the pace of innovation.
( 12
min )
In this issue: HyWay enables hybrid mingling; Auto-Tables transforms non-relational tables into standard relational forms; training dense retrievers to identify high-quality in-context examples for LLM; improving pronunciation assessment in CAPT.
The post Research Focus: Week of August 14, 2023 appeared first on Microsoft Research.
( 10
min )
Replit aims to empower the next billion software creators. In this week’s episode of NVIDIA’s AI Podcast, host Noah Kravitz dives into a conversation with Replit CEO Amjad Masad. Masad says the San Francisco-based maker of a software development platform, which came up as a member of NVIDIA’s Inception program for startups, wants to bridge Read article >
( 5
min )
Editor’s note: This post is part of Into the Omniverse, a series focused on how artists, developers and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse. Whether animating a single 3D character or generating a group of them for industrial digitalization, creators and developers who use the popular Reallusion Read article >
( 7
min )
Customers are increasingly turning to product reviews to make informed decisions in their shopping journey, whether they’re purchasing everyday items like a kitchen towel or making major purchases like buying a car. These reviews have transformed into an essential source of information, enabling shoppers to access the opinions and experiences of other customers. As a […]
( 6
min )
Digital assets are vital visual representations of products, services, culture, and brand identity for businesses in an increasingly digital world. Digital assets, together with recorded user behavior, can facilitate customer engagement by offering interactive and personalized experiences, allowing companies to connect with their target audience on a deeper level. Efficiently discovering and searching for specific […]
( 16
min )
Demystifying Logistic Regression: Your Gateway to Binary Classification in Machine Learning
( 11
min )
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
( 10
min )
When it comes to preserving profit margins, data scientists for vehicle and parts manufacturers are sitting in the driver’s seat. Viaduct, which develops models for time-series inference, is helping enterprises harvest failure insights from the data captured on today’s connected cars. It does so by tapping into sensor data and making correlations. The four-year-old startup, Read article >
( 6
min )
The start of a new school year is an ideal time for students to upgrade their content creation, gaming and educational capabilities by picking up an NVIDIA Studio laptop, powered by GeForce RTX 40 Series graphics cards.
( 8
min )
Amazon SageMaker JumpStart is a machine learning (ML) hub offering algorithms, models, and ML solutions. With SageMaker JumpStart, ML practitioners can choose from a growing list of best performing and publicly available foundation models (FMs) such as BLOOM, Llama 2, Falcon-40B, Stable Diffusion, OpenLLaMA, Flan-T5/UL2, or FMs from Cohere and LightOn. In this post and […]
( 19
min )
In this post, we showcase how to build an end-to-end generative AI application for enterprise search with Retrieval Augmented Generation (RAG) by using Haystack pipelines and the Falcon-40b-instruct model from Amazon SageMaker JumpStart and Amazon OpenSearch Service.
( 11
min )
Innovation is increasingly driven by data. As technology advances and alters human behavior, industries collect a growing quantity of information. This data is valuable once we are able to extract actionable, meaningful insights from it – insights that can accelerate better outcomes while remaining equitable and inclusive of the populations we serve, allowing us to… Read More »AI-driven predictive analytics for revenue forecasting in healthcare
The post AI-driven predictive analytics for revenue forecasting in healthcare appeared first on Data Science Central.
( 21
min )
This PhD. Thesis concerns the study and development of hierarchical
representations for spatio-temporal visual attention modeling and understanding
in video sequences. More specifically, we propose two computational models for
visual attention. First, we present a generative probabilistic model for
context-aware visual attention modeling and understanding. Secondly, we develop
a deep network architecture for visual attention modeling, which first
estimates top-down spatio-temporal visual attention, and ultimately serves for
modeling attention in the temporal domain.
( 2
min )
In private federated learning (FL), a server aggregates differentially
private updates from a large number of clients in order to train a machine
learning model. The main challenge in this setting is balancing privacy with
both classification accuracy of the learnt model as well as the number of bits
communicated between the clients and server. Prior work has achieved a good
trade-off by designing a privacy-aware compression mechanism, called the
minimum variance unbiased (MVU) mechanism, that numerically solves an
optimization problem to determine the parameters of the mechanism. This paper
builds upon it by introducing a new interpolation procedure in the numerical
design process that allows for a far more efficient privacy analysis. The
result is the new Interpolated MVU mechanism that is more scalable, has a
better privacy-utility trade-off, and provides SOTA results on
communication-efficient private FL on a variety of datasets.
( 2
min )
As data shift or new data become available, updating clinical machine
learning models may be necessary to maintain or improve performance over time.
However, updating a model can introduce compatibility issues when the behavior
of the updated model does not align with user expectations, resulting in poor
user-model team performance. Existing compatibility measures depend on model
decision thresholds, limiting their applicability in settings where models are
used to generate rankings based on estimated risk. To address this limitation,
we propose a novel rank-based compatibility measure, $C^R$, and a new loss
function that aims to optimize discriminative performance while encouraging
good compatibility. Applied to a case study in mortality risk stratification
leveraging data from MIMIC, our approach yields more compatible models while
maintaining discriminative performance compared to existing model selection
techniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval:
$0.005$, $0.035$). This work provides new tools to analyze and update risk
stratification models used in clinical care.
( 2
min )
In this research, a comparative study of four Quantum Machine Learning (QML)
models was conducted for fraud detection in finance. We proved that the Quantum
Support Vector Classifier model achieved the highest performance, with F1
scores of 0.98 for fraud and non-fraud classes. Other models like the
Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and
Sampler QNN demonstrate promising results, propelling the potential of QML
classification for financial applications. While they exhibit certain
limitations, the insights attained pave the way for future enhancements and
optimisation strategies. However, challenges exist, including the need for more
efficient Quantum algorithms and larger and more complex datasets. The article
provides solutions to overcome current limitations and contributes new insights
to the field of Quantum Machine Learning in fraud detection, with important
implications for its future development.
( 2
min )
Stroke is a significant cause of mortality and morbidity, necessitating early
predictive strategies to minimize risks. Traditional methods for evaluating
patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II,
IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy
and interpretability. This paper proposes a novel approach: an interpretable,
attention-based transformer model for early stroke mortality prediction. This
model seeks to address the limitations of previous predictive models, providing
both interpretability (providing clear, understandable explanations of the
model) and fidelity (giving a truthful explanation of the model's dynamics from
input to output). Furthermore, the study explores and compares fidelity and
interpretability scores using Shapley values and attention-based scores to
improve model explainability. The research objectives include designing an
interpretable attention-based transformer model, evaluating its performance
compared to existing models, and providing feature importance derived from the
model.
( 2
min )
This paper presents an investigation into machine learning techniques for
violence detection in videos and their adaptation to a federated learning
context. The study includes experiments with spatio-temporal features extracted
from benchmark video datasets, comparison of different methods, and proposal of
a modified version of the "Flow-Gated" architecture called "Diff-Gated."
Additionally, various machine learning techniques, including super-convergence
and transfer learning, are explored, and a method for adapting centralized
datasets to a federated learning context is developed. The research achieves
better accuracy results compared to state-of-the-art models by training the
best violence detection model in a federated learning context.
( 2
min )
This paper demonstrates the utility of organized numerical representations of
genes in research involving flat string gene formats (i.e., FASTA/FASTQ5).
FASTA/FASTQ files have several current limitations, such as their large file
sizes, slow processing speeds for mapping and alignment, and contextual
dependencies. These challenges significantly hinder investigations and tasks
that involve finding similar sequences. The solution lies in transforming
sequences into an alternative representation that facilitates easier clustering
into similar groups compared to the raw sequences themselves. By assigning a
unique vector embedding to each short sequence, it is possible to more
efficiently cluster and improve upon compression performance for the string
representations of cDNA libraries. Furthermore, through learning alternative
coordinate vector embeddings based on the contexts of codon triplets, we can
demonstrate clustering based on amino acid properties. Finally, using this
sequence embedding method to encode barcodes and cDNA sequences, we can improve
the time complexity of the similarity search by coupling vector embeddings with
an algorithm that determines the proximity of vectors in Euclidean space; this
allows us to perform sequence similarity searches in a quicker and more modular
fashion.
( 2
min )
Source: ArabianBusiness Takeaways Artificial Intelligence (AI) continues to evolve at a rapid pace, with groundbreaking strides in generative capabilities playing a critical role in defining this ever-evolving landscape. One such transformative leap is the advent of Program-Aided Language models (PAL), an innovative solution that revolutionizes how Language Learning Models (LLMs) function. This article delves into… Read More »Pushing boundaries with Generative AI: How Program-aided Language model (PAL) enhances Large Language Models (LLMs) for superior AI performance
The post Pushing boundaries with Generative AI: How Program-aided Language model (PAL) enhances Large Language Models (LLMs) for superior AI performance appeared first on Data Science Central.
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min )
Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. When you translate from one language to another, you want your machine translation to be accurate, fluent, and most importantly contextual. Domain-specific and language-specific customizable terminology is a key requirement for many government and commercial organizations. Custom terminology […]
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min )
Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. Recently, state-of-the-art architectures like the transformer architecture are used to achieve near-human performance on NLP downstream tasks like text summarization, text classification, entity recognition, […]
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min )
Learn about the challenges of data privacy and security, and the potential of smart technologies in creating efficient, livable urban environments.
The post Understanding the future of smart cities through data science appeared first on Data Science Central.
( 20
min )
This content was given as a keynote at the Workshop of Applied Data Science for Healthcare and covered during a tutorial at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, a premier forum for advancement, education, and adoption of the discipline of knowledge discovering and data mining. Recent and noteworthy advancements in […]
The post Microsoft at KDD 2023: Advancing health at the speed of AI appeared first on Microsoft Research.
( 12
min )
In this post, we present a cross-account observability dashboard that provides a centralized view for monitoring SageMaker user activities and resources across multiple accounts. It allows the end-users and cloud management team to efficiently monitor what ML workloads are running, view the status of these workloads, and trace back different account activities at certain points of time.
( 12
min )
Rise and shine, it’s time to quake up — the GeForce NOW Ultimate KovaaK’s challenge kicks off at the QuakeCon gaming festival today, giving gamers everywhere the chance to play to their ultimate potential with ultra-high 240 frames per second streaming. On top of bragging rights, top scorers can win some sweet prizes — including Read article >
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min )
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min )
A new $5+ million partnership aims to explore ways the development of artificial intelligence (AI) can support a thriving, innovative local news field, and ensure local news organizations shape the future of this emerging technology.
( 3
min )
Sponsored Post Attend the Data Science Symposium 2022 on November 8 The Center for Business Analytics at the University of Cincinnati will present its annual Data Science Symposium 2022 on November 8. This all day in-person event will have three featured speakers and two tech talk tracks with four concurrent presentations in each track. The […]
The post Attend the Data Science Symposium 2022, November 8 in Cincinnati appeared first on Machine Learning Mastery.
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min )